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Record W2073287901 · doi:10.1021/ie0707016

Strategies to Achieve a Uniform Cell Structure with a High Void Fraction in Advanced Structural Foam Molding

2008· article· en· W2073287901 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2008
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Foaming and Composites
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMaterials scienceComposite materialVoid (composites)PorosityBlowing agentMoldNucleationMolding (decorative)PolyurethaneChemistry

Abstract

fetched live from OpenAlex

Structural foams offer numerous advantages over their solid counterparts, including greater geometrical accuracy, the absence of sink marks on the final product’s surface, lower weight (and, by extension, the need for less material), and a higher stiffness-to-weight ratio. The possibility of achieving a suitable void fraction in structural foams using conventional structural foam molding methods, however, has been of limited success; these methods allow for little control and typically yield large voids and a nonuniform cell structure. This article reports on our use of an advanced structural foam molding machine to achieve a uniform cell structure with a high void fraction. We studied the following processing parameters: injection flow rate, blowing agent content, and melt temperature. The pressure profile inside the mold cavity under various processing conditions was also investigated to elucidate cell nucleation and growth behaviors. By optimizing all processing conditions, we achieved a uniform cell structure and a very high void fraction (over 40%).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score0.928

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.029
GPT teacher head0.283
Teacher spread0.254 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it